Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Statistical feature matrix for texture analysis
CVGIP: Graphical Models and Image Processing
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Digital Picture Processing
Identification of citrus disease using color texture features and discriminant analysis
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Texture Analysis Using Generalized Co-Occurrence Matrices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compact Region Extraction Using Weighted Pixel Linking in a Pyramid
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
The Visual Computer: International Journal of Computer Graphics
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The effect of probability-distance based re-weighting of image texture features on their discrimination ability was evaluated in this study. A quadratic surface smoothing function was developed from the Bhattacharyya probability distance between two classes of images. The function was used as a re-weighting function for standard grey level co-occurrence matrix (GLCM) textural features in two cases. Case 1 involved 42 images of health and smokers' lungs. Case 2 involved 144 images of well watered; partially water stressed and dry Rhacomitrium canescens plants. Multilayer perception (MLP) neural networks (NN) classifiers based on standard weighted and probabilistic-distance based re-weighted GLCM textural features were developed for each case. The classifiers were trained and tested using the leave-one-out and the cross-validation evaluation strategies. Probability-distance based re-weighting of GLCM textural features resulted in 37% and 39.22% reduction in true classification error for cases 1 and 2, respectively. It was concluded that re-weighting image texture features based on the probability distance between the classes involved, makes the GLCM texture analysis technique more discriminative and adaptive. This modification will help to overcome the fixed nature of GLCM features which is an area of weakness open to further improvement. Although GLCM features were used in this study, the method can be extended to other co-occurrence-based methods.